A Systematic Framework for Dynamically Optimizing Multi-User Wireless Video Transmission

TL;DR

This paper introduces a comprehensive framework for optimizing multi-user wireless video transmission by modeling it as a multi-user Markov decision process, incorporating environment dynamics, and proposing an online learning algorithm for real-time adaptation.

Contribution

It formulates the multi-user wireless video transmission as a MUMDP considering heterogeneous traffic and network conditions, and develops an online learning method for dynamic policy updates.

Findings

Significantly improves video quality over traditional methods.

Efficiently handles environment dynamics and user heterogeneity.

Outperforms conventional solutions in simulations.

Abstract

In this paper, we formulate the collaborative multi-user wireless video transmission problem as a multi-user Markov decision process (MUMDP) by explicitly considering the users' heterogeneous video traffic characteristics, time-varying network conditions and the resulting dynamic coupling between the wireless users. These environment dynamics are often ignored in existing multi-user video transmission solutions. To comply with the decentralized nature of wireless networks, we propose to decompose the MUMDP into local MDPs using Lagrangian relaxation. Unlike in conventional multi-user video transmission solutions stemming from the network utility maximization framework, the proposed decomposition enables each wireless user to individually solve its own dynamic cross-layer optimization (i.e. the local MDP) and the network coordinator to update the Lagrangian multipliers (i.e. resource prices) based on not only current, but also future resource needs of all users, such that the long-term video quality of all users is maximized. However, solving the MUMDP requires statistical knowledge of the experienced environment dynamics, which is often unavailable before transmission time. To overcome this obstacle, we then propose a novel online learning algorithm, which allows the wireless users to update their policies in multiple states during one time slot. This is different from conventional learning solutions, which often update one state per time slot. The proposed learning algorithm can significantly improve the learning performance, thereby dramatically improving the video quality experienced by the wireless users over time. Our simulation results demonstrate the efficiency of the proposed MUMDP framework as compared to conventional multi-user video transmission solutions.